Moreover, in the case of social graphs and large-scale recommender systems, it is often the case that there are many possible sensitive attributes that we may want to enforce invariance constraints over.
Previous work on enforcing invariance (or “fairness”) in social applications has generally focused on situations that involve one sensitive attribute (e.g., age in the context of credit or loan decisions; [1]}), but in the context of social graph embeddings there can be an extremely large number of possible sensitive attributes. In fact, in extreme settings we may even want to be fair with respect to the existence of individual edges.
For instance, a user on a social networking platform might want that platform's recommender system to ignore the fact that they are friends with a certain other user, or that they engaged with a particular piece of content.
